Tags: VALUES

Determine latest condition of each equipment and show a month wise count

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There are 100 machines in a factory.  Every machine has different test frequency. In a given month, not every machine is tested but we still have the last known rating (from some previous month) of that machine.  We have to show the latest rating of each machine for each month in a stacked column chart. […]

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Analyse membership changes from year to year

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Assume a simple 4 column dataset as shown below.  This data shows which ID had which type of subscription in which year.  So ID A, which started as a “Free” subscriber in 2018 switched to a “Premium” subscriber in 2019 and then churned out in 2020.  Likewise, ID D which started as a “Pro” subscriber […]

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Show text entries in the value area section of a Pivot Table after meeting certain conditions

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In the value area section of a normal Pivot Table one can only show the result of aggregation functions such as SUM(), COUNT(), AVERAGE() etc.  Even if one drags a text field to the value area section of a Pivot Table, one cannot show those text fields because they automatically get counted. Consider the following […]

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Count tasks by status

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Assume a simple 3 column dataset as shown below – the date of each task and the status of that task. The objective is to get the status wise count of tasks by the last time stamp.  So for the Status “To-do”, the count should be 2 – Task ABC and DEF.  Only these two […]

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Segment towns according to volume contribution and market share with a slicer

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This post is an extension to the one I posted here – Segment towns according to volume contribution and market share. Here’s a simple dataset of Shampoo sales in the state of Rajasthan, India. For a chosen segment, one may want to segment the 4 towns based on the following conditions: Based on the two screenshots […]

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Segment towns according to volume contribution and market share

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Here’s a simple dataset of Shampoo sales in the state of Rajasthan, India. For a chosen segment, one may want to segment the 4 towns based on the following conditions: Based on the two screenshots shared above, the desired result is shown in the screenshot below: The desired result is shown in range E16:E19 and […]

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Segment customers into dynamic buckets

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Consider a 4 column table – Respondent ID, Device ID, App Name and Category.  So this dataset shows which apps are installed on which device ID by which user and which category do the apps fall into.  It is a small dataset with only 4 columns and 2,000 rows. The question on this dataset is […]

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Compute hours spent on projects given resource allocation

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In the dataset below column A has the Employee Name, column B and C are the assignment start and end dates, Column D is the location and columns E to J are the Month-Year columns.  So each row represents data for an employee on a particular project.  The numbers in range E2:J8 represent how much […]

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Customer analysis by Country and time period

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Here is a Sales dataset of 8 columns and 29 rows.  It basically details the revenue earned and cash collected by service type, Customer, Country and Period.  For a selected Country and time period, there could be customers availing of both services or of any 1 service. There are 2 broad questions that one may […]

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Compute Relative Size Factor per vendor

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Relative size factor (RSF) is a test to identify anomalies where the largest amount for subsets in a given key is outside the norm for those subsets. This test compares the top two amounts for each subset and calculates the RSF for each. In order to identify potential fraudulent activities in invoice payment data, one […]

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